{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import gc\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "\n",
    "import joblib\n",
    "from scipy.sparse import hstack\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/train.csv', encoding='utf-8')\n",
    "val   = pd.read_csv('data/val.csv',   encoding='utf-8')\n",
    "\n",
    "train['word'].fillna('空', inplace = True)\n",
    "val['word'].fillna('空', inplace = True)\n",
    "\n",
    "train['char'].apply(lambda row: ' '.join([x for x in row]))\n",
    "val['char'].apply(lambda row: ' '.join([x for x in row]))\n",
    "\n",
    "data  = pd.concat([train[['char', 'word']], val[['char', 'word']]], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_word_path = 'model/ngram/vec_word.pkl'\n",
    "vec_char_path = 'model/ngram/vec_char.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_word = TfidfVectorizer(ngram_range=(1,2), min_df=3, max_df=0.9, use_idf=1, smooth_idf=1, sublinear_tf=1)\n",
    "if os.path.exists(vec_word_path):\n",
    "    vec_word = joblib.load(vec_word_path)\n",
    "else:\n",
    "    vec_word.fit(data[\"word\"])\n",
    "trn_term_word = vec_word.transform(train[\"word\"])\n",
    "val_term_word = vec_word.transform(val[\"word\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_char = TfidfVectorizer(ngram_range=(1,4), min_df=10, max_df=0.7, use_idf=1, smooth_idf=1, sublinear_tf=1)\n",
    "if os.path.exists(vec_char_path):\n",
    "    vec_char = joblib.load(vec_char_path)\n",
    "else:\n",
    "    vec_char.fit(data[\"char\"])\n",
    "trn_term_char = vec_char.transform(train[\"char\"])\n",
    "val_term_char = vec_char.transform(val[\"char\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(vec_word_path):\n",
    "    joblib.dump(vec_word, vec_word_path)\n",
    "if not os.path.exists(vec_char_path):\n",
    "    joblib.dump(vec_char, vec_char_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del data\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trn_term_word_char = hstack((trn_term_word, trn_term_char))\n",
    "val_term_word_char = hstack((val_term_word, val_term_char))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del trn_term_word\n",
    "del trn_term_char\n",
    "del val_term_word\n",
    "del val_term_char\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练逻辑回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164151, 122513)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trn_term_word_char.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_mdl = LogisticRegression(C=4, dual=False)\n",
    "lr_mdl.fit(trn_term_word_char, train[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_word_char_ngram = 'model/ngram/lr_word_char_ngram.pkl'\n",
    "if not os.path.exists(lr_word_char_ngram):\n",
    "    joblib.dump(lr_mdl, lr_word_char_ngram)\n",
    "else:\n",
    "    lr_mdl = joblib.load(lr_word_char_ngram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 123 ms, sys: 73 µs, total: 123 ms\n",
      "Wall time: 122 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "preds_val = lr_mdl.predict_proba(val_term_word_char)\n",
    "preds_val = np.argmax(preds_val, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7290771090811392\n",
      "0.6364241071681523\n",
      "0.5925741399963718\n",
      "0.6066665136438549\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(val['class'], preds_val))\n",
    "print(metrics.precision_score(val['class'], preds_val, average='macro'))\n",
    "print(metrics.recall_score(val['class'], preds_val, average='macro'))\n",
    "print(metrics.f1_score(val['class'], preds_val, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = joblib.load('model/class_le.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.47      0.60      0.53       385\n",
      "           1       0.62      0.23      0.33       546\n",
      "           2       0.63      0.57      0.60       195\n",
      "           3       0.62      0.65      0.63       917\n",
      "           4       0.59      0.53      0.56       154\n",
      "           5       0.50      0.36      0.42       505\n",
      "           6       0.56      0.53      0.55       588\n",
      "           7       0.67      0.63      0.65       371\n",
      "           8       0.80      0.74      0.77       661\n",
      "           9       0.49      0.21      0.30       291\n",
      "          10       0.89      0.90      0.89       656\n",
      "          11       0.51      0.51      0.51      1949\n",
      "          12       0.95      0.82      0.88       228\n",
      "          13       0.96      0.96      0.96     10370\n",
      "          14       0.58      0.67      0.62      2884\n",
      "          15       0.61      0.69      0.65      2719\n",
      "          16       0.54      0.45      0.49       133\n",
      "          17       0.56      0.55      0.55       363\n",
      "          18       0.89      0.92      0.90       610\n",
      "          19       0.71      0.73      0.72       682\n",
      "          20       0.61      0.46      0.52       417\n",
      "          21       0.62      0.65      0.64       521\n",
      "          22       0.43      0.36      0.39      1128\n",
      "          23       0.70      0.72      0.71      1112\n",
      "          24       0.46      0.48      0.47      1166\n",
      "          25       0.58      0.48      0.53       225\n",
      "\n",
      "    accuracy                           0.73     29776\n",
      "   macro avg       0.64      0.59      0.61     29776\n",
      "weighted avg       0.73      0.73      0.72     29776\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(val['class'], preds_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in le.classes_:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(159,0.5,'true')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75096c6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#得到混淆矩阵\n",
    "cm = metrics.confusion_matrix(val['class'], preds_val)\n",
    "\n",
    "plt.subplots(figsize=(20,12))\n",
    "ax = sns.heatmap(pd.DataFrame(cm), annot=True, fmt='.20g')\n",
    "ax.set_title('confusion matrix') #标题\n",
    "ax.set_xlabel('predict') #x轴\n",
    "ax.set_ylabel('true') #y轴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LinearSVC模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_mdl = svm.LinearSVC(C=1.0, random_state=42)\n",
    "svc_mdl.fit(trn_term_word_char, train[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_word_char_ngram = 'model/ngram/svc_word_char_ngram.pkl'\n",
    "if not os.path.exists(svc_word_char_ngram):\n",
    "    joblib.dump(svc_mdl, svc_word_char_ngram)\n",
    "else:\n",
    "    svc_mdl = joblib.load(svc_word_char_ngram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 92 ms, sys: 0 ns, total: 92 ms\n",
      "Wall time: 90.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "svc_preds_val = svc_mdl.predict(val_term_word_char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7201437399247717\n",
      "0.608604453996203\n",
      "0.5840345961144673\n",
      "0.5918722040604781\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(val['class'], svc_preds_val))\n",
    "print(metrics.precision_score(val['class'], svc_preds_val, average='macro'))\n",
    "print(metrics.recall_score(val['class'], svc_preds_val, average='macro'))\n",
    "print(metrics.f1_score(val['class'], svc_preds_val, average='macro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_mdl = lgb.LGBMClassifier(boosting_type='gbdt', \n",
    "                   objective='multiclass',\n",
    "                   metric='multi_logloss',\n",
    "                   num_class=26,\n",
    "                   num_leaves=2**6,\n",
    "                   max_depth=-1, \n",
    "                   learning_rate=0.01,\n",
    "                   n_estimators=2000,                   \n",
    "                   subsample=0.6,\n",
    "                   subsample_freq=2,\n",
    "                   colsample_bytree=0.8,\n",
    "                   reg_lambda=5,\n",
    "                   n_jobs=20,                   \n",
    "                   silent=True, \n",
    "                   random_state=2020,\n",
    "                   min_child_samples=46,\n",
    "                   min_child_weight= 0.01            \n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's multi_logloss: 2.50839\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[2]\tvalid_0's multi_logloss: 2.45674\n",
      "[3]\tvalid_0's multi_logloss: 2.41047\n",
      "[4]\tvalid_0's multi_logloss: 2.36749\n",
      "[5]\tvalid_0's multi_logloss: 2.32779\n",
      "[6]\tvalid_0's multi_logloss: 2.29106\n",
      "[7]\tvalid_0's multi_logloss: 2.25653\n",
      "[8]\tvalid_0's multi_logloss: 2.22326\n",
      "[9]\tvalid_0's multi_logloss: 2.19237\n",
      "[10]\tvalid_0's multi_logloss: 2.16165\n",
      "[11]\tvalid_0's multi_logloss: 2.13281\n",
      "[12]\tvalid_0's multi_logloss: 2.10549\n",
      "[13]\tvalid_0's multi_logloss: 2.07931\n",
      "[14]\tvalid_0's multi_logloss: 2.05354\n",
      "[15]\tvalid_0's multi_logloss: 2.02935\n",
      "[16]\tvalid_0's multi_logloss: 2.00621\n",
      "[17]\tvalid_0's multi_logloss: 1.98403\n",
      "[18]\tvalid_0's multi_logloss: 1.96263\n",
      "[19]\tvalid_0's multi_logloss: 1.94217\n",
      "[20]\tvalid_0's multi_logloss: 1.92234\n",
      "[21]\tvalid_0's multi_logloss: 1.90309\n",
      "[22]\tvalid_0's multi_logloss: 1.8845\n",
      "[23]\tvalid_0's multi_logloss: 1.8663\n",
      "[24]\tvalid_0's multi_logloss: 1.84873\n",
      "[25]\tvalid_0's multi_logloss: 1.83195\n",
      "[26]\tvalid_0's multi_logloss: 1.81538\n",
      "[27]\tvalid_0's multi_logloss: 1.79916\n",
      "[28]\tvalid_0's multi_logloss: 1.7835\n",
      "[29]\tvalid_0's multi_logloss: 1.76816\n",
      "[30]\tvalid_0's multi_logloss: 1.7533\n",
      "[31]\tvalid_0's multi_logloss: 1.73881\n",
      "[32]\tvalid_0's multi_logloss: 1.72473\n",
      "[33]\tvalid_0's multi_logloss: 1.71081\n",
      "[34]\tvalid_0's multi_logloss: 1.69722\n",
      "[35]\tvalid_0's multi_logloss: 1.68411\n",
      "[36]\tvalid_0's multi_logloss: 1.67143\n",
      "[37]\tvalid_0's multi_logloss: 1.65889\n",
      "[38]\tvalid_0's multi_logloss: 1.64659\n",
      "[39]\tvalid_0's multi_logloss: 1.63457\n",
      "[40]\tvalid_0's multi_logloss: 1.62289\n",
      "[41]\tvalid_0's multi_logloss: 1.61152\n",
      "[42]\tvalid_0's multi_logloss: 1.60032\n",
      "[43]\tvalid_0's multi_logloss: 1.58938\n",
      "[44]\tvalid_0's multi_logloss: 1.57871\n",
      "[45]\tvalid_0's multi_logloss: 1.56823\n",
      "[46]\tvalid_0's multi_logloss: 1.55804\n",
      "[47]\tvalid_0's multi_logloss: 1.54812\n",
      "[48]\tvalid_0's multi_logloss: 1.53818\n",
      "[49]\tvalid_0's multi_logloss: 1.52865\n",
      "[50]\tvalid_0's multi_logloss: 1.51926\n",
      "[51]\tvalid_0's multi_logloss: 1.50997\n",
      "[52]\tvalid_0's multi_logloss: 1.50104\n",
      "[53]\tvalid_0's multi_logloss: 1.49206\n",
      "[54]\tvalid_0's multi_logloss: 1.48334\n",
      "[55]\tvalid_0's multi_logloss: 1.47473\n",
      "[56]\tvalid_0's multi_logloss: 1.46642\n",
      "[57]\tvalid_0's multi_logloss: 1.45817\n",
      "[58]\tvalid_0's multi_logloss: 1.45015\n",
      "[59]\tvalid_0's multi_logloss: 1.44233\n",
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      "[1079]\tvalid_0's multi_logloss: 0.84971\n",
      "[1080]\tvalid_0's multi_logloss: 0.849707\n",
      "[1081]\tvalid_0's multi_logloss: 0.849718\n",
      "[1082]\tvalid_0's multi_logloss: 0.849706\n",
      "[1083]\tvalid_0's multi_logloss: 0.849703\n",
      "[1084]\tvalid_0's multi_logloss: 0.849707\n",
      "[1085]\tvalid_0's multi_logloss: 0.849698\n",
      "[1086]\tvalid_0's multi_logloss: 0.849705\n",
      "[1087]\tvalid_0's multi_logloss: 0.849705\n",
      "[1088]\tvalid_0's multi_logloss: 0.849723\n",
      "[1089]\tvalid_0's multi_logloss: 0.849716\n",
      "[1090]\tvalid_0's multi_logloss: 0.84971\n",
      "[1091]\tvalid_0's multi_logloss: 0.849692\n",
      "[1092]\tvalid_0's multi_logloss: 0.849705\n",
      "[1093]\tvalid_0's multi_logloss: 0.849736\n",
      "[1094]\tvalid_0's multi_logloss: 0.849752\n",
      "[1095]\tvalid_0's multi_logloss: 0.84975\n",
      "[1096]\tvalid_0's multi_logloss: 0.849754\n",
      "[1097]\tvalid_0's multi_logloss: 0.849746\n",
      "[1098]\tvalid_0's multi_logloss: 0.849732\n",
      "[1099]\tvalid_0's multi_logloss: 0.849719\n",
      "[1100]\tvalid_0's multi_logloss: 0.849708\n",
      "[1101]\tvalid_0's multi_logloss: 0.849709\n",
      "[1102]\tvalid_0's multi_logloss: 0.849708\n",
      "[1103]\tvalid_0's multi_logloss: 0.849685\n",
      "[1104]\tvalid_0's multi_logloss: 0.849698\n",
      "[1105]\tvalid_0's multi_logloss: 0.849697\n",
      "[1106]\tvalid_0's multi_logloss: 0.849696\n",
      "[1107]\tvalid_0's multi_logloss: 0.849708\n",
      "[1108]\tvalid_0's multi_logloss: 0.849712\n",
      "[1109]\tvalid_0's multi_logloss: 0.849731\n",
      "[1110]\tvalid_0's multi_logloss: 0.849773\n",
      "[1111]\tvalid_0's multi_logloss: 0.84978\n",
      "[1112]\tvalid_0's multi_logloss: 0.849815\n",
      "[1113]\tvalid_0's multi_logloss: 0.849849\n",
      "[1114]\tvalid_0's multi_logloss: 0.849861\n",
      "[1115]\tvalid_0's multi_logloss: 0.849877\n",
      "[1116]\tvalid_0's multi_logloss: 0.849887\n",
      "[1117]\tvalid_0's multi_logloss: 0.849865\n",
      "[1118]\tvalid_0's multi_logloss: 0.849848\n",
      "[1119]\tvalid_0's multi_logloss: 0.849847\n",
      "[1120]\tvalid_0's multi_logloss: 0.849811\n",
      "[1121]\tvalid_0's multi_logloss: 0.849781\n",
      "[1122]\tvalid_0's multi_logloss: 0.849786\n",
      "[1123]\tvalid_0's multi_logloss: 0.849791\n",
      "[1124]\tvalid_0's multi_logloss: 0.849836\n",
      "[1125]\tvalid_0's multi_logloss: 0.849847\n",
      "[1126]\tvalid_0's multi_logloss: 0.849855\n",
      "[1127]\tvalid_0's multi_logloss: 0.84987\n",
      "[1128]\tvalid_0's multi_logloss: 0.849875\n",
      "[1129]\tvalid_0's multi_logloss: 0.849869\n",
      "[1130]\tvalid_0's multi_logloss: 0.849851\n",
      "[1131]\tvalid_0's multi_logloss: 0.849843\n",
      "[1132]\tvalid_0's multi_logloss: 0.849823\n",
      "[1133]\tvalid_0's multi_logloss: 0.849842\n",
      "[1134]\tvalid_0's multi_logloss: 0.84984\n",
      "[1135]\tvalid_0's multi_logloss: 0.849852\n",
      "[1136]\tvalid_0's multi_logloss: 0.849865\n",
      "[1137]\tvalid_0's multi_logloss: 0.84984\n",
      "[1138]\tvalid_0's multi_logloss: 0.849842\n",
      "[1139]\tvalid_0's multi_logloss: 0.849826\n",
      "[1140]\tvalid_0's multi_logloss: 0.849805\n",
      "[1141]\tvalid_0's multi_logloss: 0.849829\n",
      "[1142]\tvalid_0's multi_logloss: 0.849855\n",
      "[1143]\tvalid_0's multi_logloss: 0.849837\n",
      "[1144]\tvalid_0's multi_logloss: 0.849844\n",
      "[1145]\tvalid_0's multi_logloss: 0.849838\n",
      "[1146]\tvalid_0's multi_logloss: 0.849835\n",
      "[1147]\tvalid_0's multi_logloss: 0.849829\n",
      "[1148]\tvalid_0's multi_logloss: 0.849833\n",
      "[1149]\tvalid_0's multi_logloss: 0.84985\n",
      "[1150]\tvalid_0's multi_logloss: 0.849848\n",
      "[1151]\tvalid_0's multi_logloss: 0.849874\n",
      "[1152]\tvalid_0's multi_logloss: 0.849876\n",
      "[1153]\tvalid_0's multi_logloss: 0.849872\n",
      "[1154]\tvalid_0's multi_logloss: 0.849866\n",
      "[1155]\tvalid_0's multi_logloss: 0.849889\n",
      "[1156]\tvalid_0's multi_logloss: 0.849907\n",
      "[1157]\tvalid_0's multi_logloss: 0.849897\n",
      "[1158]\tvalid_0's multi_logloss: 0.84989\n",
      "[1159]\tvalid_0's multi_logloss: 0.84989\n",
      "[1160]\tvalid_0's multi_logloss: 0.849908\n",
      "[1161]\tvalid_0's multi_logloss: 0.849883\n",
      "[1162]\tvalid_0's multi_logloss: 0.849888\n",
      "[1163]\tvalid_0's multi_logloss: 0.849885\n",
      "[1164]\tvalid_0's multi_logloss: 0.849869\n",
      "[1165]\tvalid_0's multi_logloss: 0.849864\n",
      "[1166]\tvalid_0's multi_logloss: 0.849861\n",
      "[1167]\tvalid_0's multi_logloss: 0.849869\n",
      "[1168]\tvalid_0's multi_logloss: 0.849853\n",
      "[1169]\tvalid_0's multi_logloss: 0.849849\n",
      "[1170]\tvalid_0's multi_logloss: 0.849867\n",
      "[1171]\tvalid_0's multi_logloss: 0.849875\n",
      "[1172]\tvalid_0's multi_logloss: 0.849894\n",
      "[1173]\tvalid_0's multi_logloss: 0.849913\n",
      "[1174]\tvalid_0's multi_logloss: 0.849943\n",
      "[1175]\tvalid_0's multi_logloss: 0.849935\n",
      "[1176]\tvalid_0's multi_logloss: 0.849934\n",
      "[1177]\tvalid_0's multi_logloss: 0.849923\n",
      "[1178]\tvalid_0's multi_logloss: 0.849914\n",
      "[1179]\tvalid_0's multi_logloss: 0.849909\n",
      "[1180]\tvalid_0's multi_logloss: 0.849916\n",
      "[1181]\tvalid_0's multi_logloss: 0.849922\n",
      "[1182]\tvalid_0's multi_logloss: 0.849952\n",
      "[1183]\tvalid_0's multi_logloss: 0.849953\n",
      "[1184]\tvalid_0's multi_logloss: 0.849983\n",
      "[1185]\tvalid_0's multi_logloss: 0.849974\n",
      "[1186]\tvalid_0's multi_logloss: 0.849972\n",
      "[1187]\tvalid_0's multi_logloss: 0.849983\n",
      "[1188]\tvalid_0's multi_logloss: 0.849972\n",
      "[1189]\tvalid_0's multi_logloss: 0.849964\n",
      "[1190]\tvalid_0's multi_logloss: 0.849981\n",
      "[1191]\tvalid_0's multi_logloss: 0.850012\n",
      "[1192]\tvalid_0's multi_logloss: 0.850023\n",
      "[1193]\tvalid_0's multi_logloss: 0.850015\n",
      "[1194]\tvalid_0's multi_logloss: 0.850011\n",
      "[1195]\tvalid_0's multi_logloss: 0.850007\n",
      "[1196]\tvalid_0's multi_logloss: 0.849994\n",
      "[1197]\tvalid_0's multi_logloss: 0.849992\n",
      "[1198]\tvalid_0's multi_logloss: 0.849972\n",
      "[1199]\tvalid_0's multi_logloss: 0.849981\n",
      "[1200]\tvalid_0's multi_logloss: 0.850012\n",
      "[1201]\tvalid_0's multi_logloss: 0.850043\n",
      "[1202]\tvalid_0's multi_logloss: 0.850056\n",
      "[1203]\tvalid_0's multi_logloss: 0.850043\n",
      "Early stopping, best iteration is:\n",
      "[1103]\tvalid_0's multi_logloss: 0.849685\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(colsample_bytree=0.8, learning_rate=0.01, metric='multi_logloss',\n",
       "               min_child_samples=46, min_child_weight=0.01, n_estimators=2000,\n",
       "               n_jobs=20, num_class=26, num_leaves=64, objective='multiclass',\n",
       "               random_state=2020, reg_lambda=5, subsample=0.6,\n",
       "               subsample_freq=2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_mdl.fit(trn_term_word_char, train[\"class\"], eval_set=(val_term_word_char, val[\"class\"]), early_stopping_rounds=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_word_char_ngram = 'model/ngram/lgb_word_char_ngram.pkl'\n",
    "if not os.path.exists(lgb_word_char_ngram):\n",
    "    joblib.dump(lgb_mdl, lgb_word_char_ngram)\n",
    "else:\n",
    "    lgb_mdl = joblib.load(lgb_word_char_ngram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21min 28s, sys: 4.31 s, total: 21min 32s\n",
      "Wall time: 21 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lgb_preds_val = lgb_mdl.predict_proba(val_term_word_char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_preds_val = np.argmax(lgb_preds_val, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7408987103707684\n",
      "0.6387178269965273\n",
      "0.6051682216597312\n",
      "0.6163556479035844\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(val['class'], lgb_preds_val))\n",
    "print(metrics.precision_score(val['class'], lgb_preds_val, average='macro'))\n",
    "print(metrics.recall_score(val['class'], lgb_preds_val, average='macro'))\n",
    "print(metrics.f1_score(val['class'], lgb_preds_val, average='macro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评估测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test  = pd.read_csv('data/test.csv',  encoding='utf-8')\n",
    "test['word'].fillna('空', inplace = True)\n",
    "test['char'].apply(lambda row: ' '.join([x for x in row]))\n",
    "\n",
    "test_term_word = vec_word.transform(test[\"word\"])\n",
    "test_term_char = vec_char.transform(test[\"char\"])\n",
    "\n",
    "test_term_word_char = hstack((test_term_word, test_term_char))\n",
    "\n",
    "del test_term_word\n",
    "del test_term_char\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 197 ms, sys: 12 ms, total: 209 ms\n",
      "Wall time: 208 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "preds_test = lr_mdl.predict_proba(test_term_word_char)\n",
    "preds_test = np.argmax(preds_test, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7190671774416493\n",
      "0.6074103855860521\n",
      "0.5714875276493385\n",
      "0.5796201724481629\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(test['class'], preds_test))\n",
    "print(metrics.precision_score(test['class'], preds_test, average='macro'))\n",
    "print(metrics.recall_score(test['class'], preds_test, average='macro'))\n",
    "print(metrics.f1_score(test['class'], preds_test, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 144 ms, sys: 9.9 ms, total: 154 ms\n",
      "Wall time: 153 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "preds_test_svc = svc_mdl.predict(test_term_word_char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7110303700485763\n",
      "0.586581253211084\n",
      "0.573956053402969\n",
      "0.5735488632254664\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(test['class'], preds_test_svc))\n",
    "print(metrics.precision_score(test['class'], preds_test_svc, average='macro'))\n",
    "print(metrics.recall_score(test['class'], preds_test_svc, average='macro'))\n",
    "print(metrics.f1_score(test['class'], preds_test_svc, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 36min 10s, sys: 52.4 s, total: 37min 2s\n",
      "Wall time: 35.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "preds_test_lgb = lgb_mdl.predict_proba(test_term_word_char)\n",
    "preds_test_lgb = np.argmax(preds_test_lgb, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7296907705066941\n",
      "0.6146837496323864\n",
      "0.5834968546695235\n",
      "0.5897043488107605\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(test['class'], preds_test_lgb))\n",
    "print(metrics.precision_score(test['class'], preds_test_lgb, average='macro'))\n",
    "print(metrics.recall_score(test['class'], preds_test_lgb, average='macro'))\n",
    "print(metrics.f1_score(test['class'], preds_test_lgb, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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